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This paper tests two moving average technical trading rules for four Asian markets. Our results indicate that moving average rules do indeed have predictive power and can discern recurring price patterns for profitable trading. Moreover, our results support the hypothesis that technical trading rules can outperform the buy‐and‐hold strategy. Break‐even one‐way trading costs are estimated to be high for all four markets. To confirm the test outcome, robust tests based on bootstrap and the related t‐tests among the markets are also carried out. We conclude from the statistical results that moving average rules are valid and indeed have predictive power. It is implied that the trading rules may be used to design a trading strategy that will beat the buy‐and‐hold strategy in the Hong Kong, Singapore, South Korea, and Taiwan markets. The contribution of the current study is that this is the first validation test of trading rules using four markets at a similar development stage and culture tradition; and in the tests, we use most current and longer periods than the periods used in previous literature. Our robust tests are unique and considered distribution‐free.

This article investigates the profitability of technical trading rules in the KOSPI200 futures market from 1997 through 2006 after accounting for transaction costs, risk. and data-snooping problems. To effectively mitigate data - snooping problems resulted from survivorship bias, we largely expand the full set of technical trading rules handled in the previous literature and measure statistical significance of technical trading performance using White’s (2000) Bootstrap Reality Check (BRC) methodology and Hansen’s (2005) Superior Predictive Ability (SPA) test that can take account of interdependency across individual technical trading rules.

The results indicate that under the net return criterion the best trading rule generates the highest mean net return of about 32% per annum during the sample period but the trading return is statistically insignificant when the effect of data-snooping is considered. Similar results are found under the Sharpe ratio criterion. These findings suggest that substantial technical trading profits may be obtained due to chance rather than the Inherent predictability of technical trading rules.

The purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume.

This paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests.

The result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection.

Unlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

Outlines previous research on stock market efficiency and technical trading rules in both developed and emerging markets. Uses variable moving average (VMA) models to develop five technical trading rules and applies them to markets in Taiwan, Thailand and The Phillippines 1994‐1999. Compares results with the US and Japan indices and a simple buy and hold strategy. Finds the VMA rules gave higher returns in Taiwan and very much higher returns in Thailand and The Phillippines, even after transaction costs, but not in Japan and the USA. Considers the reasons why and calls for further research.

Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

Questionnaire surveys made at currency markets around the world reveal that currency trade to a large extent not only is determined by an economy's performance or expected performance. Indeed, a fraction is guided by technical trading, which means that past exchange rates are assumed to provide information about future exchange rate movements. The purpose of this paper is to ask how a successful monetary policy should be designed when technical trading in the form of trend following is used in currency trading.

The paper embeds an optimal policy rule into Galí and Monacelli's dynamic stochastic general equilibrium (DSGE) model for a small open economy, which is augmented with trend following in currency trading, to examine the prerequisites for a successful monetary policy. Specifically, the conditions for a determinate rational expectations equilibrium (REE) that also is stable under least squares learning are in focus. The paper also computes impulse‐response functions for key variables to study how the economy returns to steady state after being hit by a shock.

The paper finds that a determinate REE that also is stable under least squares learning often is the outcome when there is a limited amount of trend following in currency trading, but that a more flexible inflation rate targeting in monetary policy sometimes cause an indeterminate REE in the economy. Thus, strict, or almost strict, inflation rate targeting in monetary policy is recommended also when there is technical trading in currency trading and not only when all currency trading is guided by fundamental analysis (in the form of rational expectations). This result is a new result in the literature.

There are already models in the literature on monetary policy design that incorporate technical trading in currency trading into an otherwise standard DSGE model. There is also a huge amount of DSGE models in the literature in which monetary policy is optimal. However, the model in this paper is the first model, to the best of the author's knowledge, where technical trading in currency trading and optimal monetary policy are combined in the same DSGE model.

To investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.

The conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.

The empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.

New results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

This study takes a novel approach to testing the efficacy of technical analysis. Rather than testing specific trading rules as is typically done in the literature, we rely on institutional portfolio managers’ statements about whether and how intensely they use technical analysis, irrespective of the form in which they implement it. In our sample of more than 10,000 portfolios, about one-third of actively managed equity and balanced funds use technical analysis. We compare the investment performance of funds that use technical analysis versus those that do not, using five metrics. Mean and median (3 and 4-factor) alpha values are generally slightly higher for a cross section of funds using technical analysis, but performance volatility is also higher. Benchmark-adjusted returns are also higher, particularly when market prices are declining. The most remarkable finding is that portfolios with greater reliance on technical analysis have elevated skewness and kurtosis levels relative to portfolios that do not use technical analysis. Funds using technical analysis appear to have provided a meaningful advantage to their investors, albeit in an unexpected way.

We model international short-term capital flow by identifying technical trading rules in short-term capital markets using Genetic Programming (GP). The simulation results suggest that the international short-term markets was quite efficient during the period of 1997–2002, with most GP generated trading strategies recommending buy-and-hold on one or two assets. The out-of-sample performance of GP trading strategies varies from year to year. However, many of the strategies are able to forecast Taiwan stock market down time and avoid making futile investment. Investigation of Automatically Defined Functions shows that they do not give advantages or disadvantages to the GP results.

The purpose of this paper is to establish two competitive models to explain why investors use technical analysis (TA).

Information Discovery Model suggests that technical traders are able to infer non-public information; Herding Behavior Model argues that TA is a kind of irrational herding behavior that can make profit when other noise traders exist.

The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns.

The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns. Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient.

Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient.